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access icon free Region-based saliency detection

In this study, the authors propose an unsupervised approach to detect saliency of each pixel in an image. The proposed region-based pixel-wise saliency detection approach produces full resolution (same as that of the original image) saliency map and precisely locates visually prominent region/object of interest in the input image. There are two parts in the authors approach. In the first phase, they partition the input image into homogeneous regions using split-and-merge technique. In the second phase, they rank the regions based on its proximity to the centre of the image, visual significance, size and completeness. Based on the ranking of the regions, the significance of each pixel is computed. The proposed saliency detection approaches improves the accuracy of content-based applications such as salient object segmentation and content aware image resizing. Experimental results show that their proposed approach qualitatively better than the state-of-art approaches and quantitatively comparable to ground truth information which are collected from human observers.

References

    1. 1)
      • 1. Ouerhani, N., Bracamonte, J., Hügli, H., Ansorge, M., Pellandini, F.: ‘Adaptive color image compression based on visual attention’. Proc. 11th Int. Conf. Image Analysis and Processing (ICIAP, Palermo, IEEE Computer society press), September 2001, pp. 416421.
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
      • 29. Ge, F., Wang, S., Liu, T.: ‘Image-segmentation evaluation from the perspective of salient object extraction’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR'06), June 2006, vol. 1, pp. 11461153.
    7. 7)
    8. 8)
    9. 9)
      • 19. Scholl, B.J.: ‘Objects and attention: the state of the art’, Cognition, 2001, 80, pp. 146.
    10. 10)
    11. 11)
    12. 12)
      • 8. Meur, O.L., Callet, P.L.: ‘What we see is most likely to be what matters: visual attention and applications’. 16th IEEE Int. Conf. on Image Processing (ICIP), Cairo, November 2009, pp. 30853088.
    13. 13)
      • 2. Zhang, Q., Gu, G., Xiao, H.: ‘Image segmentation based on visual attention mechanism’, J. Multimed., 2009, 4, (6), pp. 363370.
    14. 14)
    15. 15)
      • 22. Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: ‘Frequency-tuned salient region detection’. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Miami, June 2009, pp. 15971604.
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
      • 20. Judd, T., Ehinger, K., Durand, F., Torralba, A.: ‘Learning to predict where humans look’. Proc. IEEE Int. Conf. Computer Vision, September 2009, pp. 21062113.
    22. 22)
    23. 23)
      • 12. Hou, X., Zhang, L.: ‘Saliency detection: a spectral residual approach’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR'07), Minneapolis, June 2007, pp. 18.
    24. 24)
      • 25. Rubinstein, M., Gutierrez, D., Sorkine, O., Shamir, A.: ‘A comparative study of image retargeting’. Proc. SIGGRAPH Asia'10, article no. 160, 2010.
    25. 25)
    26. 26)
      • 17. Seber, G.A.F.: ‘Multivariate observations’ (John Wiley & Sons, Inc., 1984).
    27. 27)
      • 24. Vaquero, D., Turk, M., Pulli, K., Tico, M., Gelfand, N.: ‘A survey of image retargeting techniques’. Proc. SPIE 7798, Applications of Digital Image Processing XXXIII, September 2010, pp. 779814.
    28. 28)
      • 15. Manipoonchelvi, P., Muneeswaran, K.: ‘Significant region based image retrieval using Curvelet transform’. Int. Conf. on Recent Advancements in Electrical, Electronics, and Control Engineering (ICONRAEeCE), India, December 2011, pp. 291294.
    29. 29)
    30. 30)
      • 16. Ohashi, T., Aghbari, Z., Makinouchi, A.: ‘Hill-climbing algorithm for efficient color-based image segmentation’. IASTED Int. Conf. on Signal Processing, Pattern Recognition, and Applications (SPPRA 2003), Track no 404–059, June 2003.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2013.0434
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